Measurements are made on substrates during semiconductor manufacturing in order to control the process. As the term is used herein, “integrated circuit” includes devices such as those formed on monolithic semiconducting substrates, such as those formed of group IV materials like silicon or germanium, or group III-V compounds like gallium arsenide, or mixtures of such materials. The term includes all types of devices formed, such as memory and logic, and all designs of such devices, such as MOS and bipolar. The term also comprehends applications such as flat panel displays, solar cells, and charge coupled devices.
Typically, a pattern of measurements across the substrate is selected that is expected to adequately represent the entire substrate with the minimum number of measurements. Since these measurements are generally made on test structures that are placed in the die scribe lines (the space between active dice) the pattern of the measurement is constrained by the way that the dice are placed on the substrate, and where in the scribe line the measurement structure is located. A minimum number of measurements are desired due to the high cost of measurements. The cost is driven by cycle time, equipment costs, throughput and other factors.
The pattern of measurements across the substrate is assumed to measure the non uniformity of a critical parameter such as film thickness, line width, and other critical dimensions. The hidden assumption is that the pattern across the substrate is relatively smoothly varying, or is detectable according to some other anticipated characteristic.
In practice, a desirable aspect of the substrate measurements is to detect the presence of a characteristic spatial pattern across the substrate that represents a shift in the process. Substrate processes can fail to be uniform in a number of different patterns that are often characteristic of the process or equipment being used. For example, a chemical mechanical planarization process may often deviate from uniformity in a doughnut or ring type of pattern. A photolithography process may deviate from uniformity in a checkerboard pattern, due to factors such as mask misalignment or ill-conditioned light exposure. Oxidation or other high-temperature processes can produce linear thickness gradients across the substrate or spots of thickness variation near boat contacts.
Once a process is established in manufacturing, the objective of taking measurements is often no longer to establish the uniformity of the process, but rather to detect variations from the normal uniformity. The nonuniformity is usually a limitation of the process equipment. The integration of a nonuniform process with a down stream nonuniform process can be beneficial for product yield because the nonuniformities can be controlled to compensate for each other. Early detection of certain patterns that indicate a process drift is valuable. Often the pattern will indicate the specific source of the drift.
Variations in integrated circuit processing on substrates frequently follow patterns across the substrate. Excessive variation can lead to device failure, lower yields, and reduced value of the substrate. In order to monitor and control processes variations, the typical practice in the industry is to reduce the measurement results to summary statistics, such as average and range (maximum value-minimum value). There values are in turn plotted on a statistical process control chart for monitoring and controlling process variation. Countermeasures are taken when these values vary outside of the normal distribution of data.
The problem with this approach is that when processes start to drift out of control, this method might not detect any characteristic pattern that might be dictated by the physical configuration of the process equipment. The measurement sample plan may or may not be sensitive to the changing pattern. In addition, the reduction of the measurement values to simple means and ranges can further reduce the ability to identify a failure or detect subtle patterns.
Statistical process control approaches have also been used to detect process variation, but are not sensitive to the emergence of a specific pattern that is known to signify pending process failure. If the statistical process control limits are set tighter, they may signal the failure, but at the expense of detecting other variations that are normal and do not signal a failure (a false positive).
In current practice, the selection of measurement patterns is heavily weighted towards minimizing the costs of taking the measurement. However, additional consideration should be given to the measurement strategy, if the selected sample plan can actually detect patterns of interest.
Different methods have been developed to compensate for this failure. For example, there is a variety of existing sample plans that are available from metrology tool manufacturers that attempt to capture patterns. However, these are fixed patterns that do not take in to account the die size or location. They are not optimized for specific patterns that are on the substrates.
Another method is user definable custom measurement patterns. Engineers can eyeball the pattern on a substrate with a larger number of measurements and estimate a plan for a reduced number of measurements that appears to represent the typical process pattern. While an informed engineering decision determining sample pattern can be effective, this is not a systematic approach and provides no figures of merit for different sample plans to make a decision with. No algorithms exist for selecting measurement locations.
Finally, increasing the number of measurements across the substrate in an effort to increase the probability of detecting spatial patterns of interest has also been attempted. However, increasing the number of measurements reduces the throughput of the equipment, and may mean that more equipment must be purchased and slows down process cycle times. This adds to the substrate cost.
What is needed, therefore, is a system of inspection site location determination and measurement analysis that overcomes problems such as those described above, at least in part.